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Speed Control of Vane-Type Air Motor Servo System Using Proportional-Integral-Derivative-Based Fuzzy Neural Network

Abstract

A novel proportional-integral-derivative-based fuzzy neural network (PID-based FNN) controller is proposed in this study to control the speed of a vane-type air motor (VAM) servo system for tracking periodic speed command. First, the structure and operating principles of the VAM servo system are introduced. Then, the dynamics of the VAM servo system is analyzed to derive the second-order state equation of the VAM. Moreover, due to the dynamic characteristics and system parameters of the VAM servo system are highly nonlinear and time-varying, a PID-based FNN controller, which integrates conventional proportional-integral-derivative neural network (PIDNN) control with fuzzy rules, is proposed to achieve precise speed control of VAM servo system under the occurrences of the inherent nonlinearities and external disturbances. The network structure and its on-line learning algorithm using delta adaptation law are described in detail. Meanwhile, the convergence analysis of the speed tracking error is given using the discrete-type Lyapunov function. To enhance the control performance of the proposed intelligent control approach, a 32-bit floating-point digital signal processor (DSP), TMS320F28335, is adopted for the implementation of the proposed control system. Finally, experimental results are illustrated to show the validity and advantages of the proposed PID-based FNN controller for VAM servo system.

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Acknowledgments

The authors would like to acknowledge the financial support of the Ministry of Science and Technology in Taiwan, R.O.C. through its Grant MOST 103-2218-E-003 -001.

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Correspondence to Syuan-Yi Chen.

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Chen, SY., Hung, YH. & Gong, SS. Speed Control of Vane-Type Air Motor Servo System Using Proportional-Integral-Derivative-Based Fuzzy Neural Network. Int. J. Fuzzy Syst. 18, 1065–1079 (2016). https://doi.org/10.1007/s40815-015-0134-0

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  • DOI: https://doi.org/10.1007/s40815-015-0134-0

Keywords

  • Fuzzy neural network (FNN)
  • Proportional-integral-derivative neural network (PIDNN)
  • Vane-type air motor (VAM)
  • Speed tracking control